DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Instant application eligible to benefit from the foreign application as claimed by applicant on 09/21/2020 and effective filling date was considered as 09/21/2020.
Information Disclosure Statement
IDS has been submitted on 03/17/2023, 07/17/2024, 12/12/2024, 03/06/2025, 12/19/2025, and 04/23/2026 and considered by the examiner.
Claim Status
Claims 1-17 are pending and examined on the merits.
Claim 1-17 are rejected.
Claim Objections
Claim 16 objected to because of the following informalities: The phrase “configured to import import data” contains a repeated word “import”. This should be objected to for clarity and formally corrected to "import data". Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, the instant claims 1-15, are drawn to a process (method), claims 16 and 17 are drawn to a system, and therefore are found to recite statutory subject matter (Step 1: YES). The instant claims are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). The instant claims recite the following limitations that equate to an abstract idea:
Claim 1 recites
An extraction step in which an extraction processor extracts, a region common to the scan data from the import data. (Mental process)
An alignment step in which an alignment processor performs alignment based on the region common to the import data and the scan data. (Mental process)
Claim 2 recites “the extraction step comprises generating, by the extraction processor, the feature information from the import data, and the feature information comprises at least one selected from the group of surface curvature information, surface corrugation information, and user input information.” (Mental process) Determining surface curvature or corrugation is a classic analytical task that humans do, making it an ineligible mental step.
Claim 3 recites
In the extraction step, the extraction processor generates at least one selected from the group of surface curvature information and surface corrugation information formed by connecting adjacent points of the import data, as feature information of a three-dimensional shape of the object. (Mental process) The step involves connecting adjacent points to generate “surface curvature” and “surface corrugation.”
In the alignment step, the import data and the scan data are aligned with each other by identifying common points based on the import data and feature information of the scan data and mapping the common points to each other. (Mental process and mathematical concept)
Claim 5 recites “comprising an editing step in which an editing tool receives an editing command to edit a part of the import data and updates the import data.(Mental process)
Claim 7 recites “wherein in the display step, the display processor voxelizes each of the import data and scan data and displays the voxelized data on the display device.” (Mathematical concept). Voxelating data involves converting it into a 3D grid, which is a mathematical and spatial transformation.
Claim 10 recites “in the display step, the reliability of the import data is set to a highest value in a range of predetermined numerical values.” (Mental process and mathematical concept) Under USPTO guidelines, setting the reliability of imported data to its highest predetermined numerical value in a display step is an abstract idea.
Claim 11 recites “in the import data reception step, the data processor further receives, as the import data, at least one selected from the group of a normal vector indicating directions of points of the import data and meshes formed by connecting the points.” (Mathematical concept) Normal vectors and meshes formed by connecting data points are mathematical representations of shape, geometry, and spatial direction.
Claim 12 recites
A resolution determination step in which a resolution processor determines whether a distance between adjacent points of the import data exceeds a reference distance, which is a maximum allowable distance between adjacent points of the scan data. (Mathematical concept and mental process) Evaluating or determining a numerical threshold against a reference value describes a type of comparison or judgment that can conceptually be performed in the human mind.
An import data updating step in which, when the distance between the adjacent points of the import data is determined to exceed the reference distance in the resolution determination step, the resolution processor updates the import data by generating new points between at least some points such that the distance between the adjacent points of the import data is equal to or less than the reference distance. (Mathematical concept)
The integration step is performed after the resolution determination step and the import data updating step are performed. (Mental process) Updating imported data, and integrating/combining those results are purely informational or logic-based operations that can theoretically be done in a person's head or with pen and paper.
Claim 13 recites
A common point reception step in which an input processor receives, through an input device, at least one common point at places identical to each other in the three- dimensional shapes of the object defined by the import data and the scan data. (Mental process)
Claim 17 recites “the extraction processor generates the feature information from the import data, and the feature information comprises at least one selected from the group of surface curvature information, surface corrugation information, and user input information”. (Mental process)
As such claims 1-17 recite an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Specifically, the claims recite the following additional elements:
Claim 1 recites
A scan data reception step in which a scan data reception processor generates, by scanning the object with a three-dimensional scanner, scan data that comprises shape information and feature information of the object and is at least partially in common with the import data.
An import data reception step in which a data processor imports data comprising shape information of at least a part of an object.
A scan data reception step in which a scan data reception processor generates, by scanning the object with a three-dimensional scanner, scan data that comprises shape information and feature information of the object and is at least partially in common with the import data.
An integration step in which an integration processor generates integrated data by integrating the import data and the scan data.
Claim 4 recites “the three-dimensional scan data processing method of claim 1, further comprising a display step in which a display processor displays, on a display device, a three-dimensional shape of the object defined by at least one selected from the group of the import data and the scan data.”
Claim 6 recites “in the display step, the display processor selectively displays the three- dimensional shapes of the object, which are defined by the import data and the scan data, on the display device according to a command received through an input device.”
Claim 8 recites “in the display step, the three-dimensional shapes of the object, which are defined by the import data and the scan data, are displayed on the display device in different colors.
Claim 9 recites “in the display step, the display processor displays each region on the display device in a different color according to reliability of data, and the display processor displays a part corresponding to the import data on the display device according to predetermined reliability.”
Claim 13 recites “In the alignment step, the alignment processor aligns the import data and the scan data with each other by a method in which the input processor maps the common point of the import data and the scan data that is received in the common point reception step.
Claim 14 recites
The scan data generated by the scan data reception processor in the scan data reception step is data accumulated in real time by the three-dimensional scanner.
The alignment step and the integration step are performed while the scan data accumulated in real time by the scan data reception step is updated.
Claim 15 recites
In the import data reception step, the data processor device receives the import data without using the three-dimensional scanner.
In the scan data reception step, the scan data is generated through the three-dimensional scanner.
Claim 16 recites
A scan data reception processor configured to generate, by scanning the object with a three-dimensional scanner, scan data that comprises shape information and feature information of the object and is at least partially in common with the import data.”
A data processor configured to import data defining a three- dimensional shape including shape information of at least a part of an object.
An extraction processor configured to extract a region common to the scan data from the import data.
An alignment processor configured to perform alignment based on the region common to the import data and the scan data.
An integration processor configured to generate integrated data by integrating the import data and the scan data.
The recited additional elements of scanning an object, importing shape data, and integrating data datasets in claim 1 fail to provide a practical application under 35 U.S.C. 101 because they merely recite conventional data gathering, generic data reception, and well-understood mathematical combination as evidenced by Erten et al. review article where he describes use of 3D scanner technology to evaluate structures in real three anatomical dimensions. (Abstract; pg. 2; Figure 2D), without improving computer technology or transforming the abstract idea into a practical application.
The additional elements recited in claims 2, namely, a scan data reception step in which a scan data reception processor scanning the object with a three-dimensional scanner (Erten et al. review article) relies on routine, well-understood, and conventional techniques and fail to provide a practical application. Under the Alice framework, receiving and generating scan data constitutes standard, pre-solution activity that does not transform an abstract idea into a patentable application
For claim 4, it merely displaying, outputting, or reporting the results of an abstract idea and considered as insignificant extra-solution activity and does not render the claim patent-eligible.
For claim 6, the use of a “display processor” and “display device” is purely functional. It provides no specific improvement to computer technology itself, but rather uses generic hardware as a tool to display the results of an abstract idea.
Regarding claim 8, displaying 3D shapes in different colors on a screen is a conventional data-presentation step that merely automates human visualization, failing to integrate an abstract idea into a practical application under 35 U.S.C. 101. Changing display colors relies on generic, well-known graphical user interface capabilities rather than a specialized, non-conventional technological process.
For claim 9, the additional element does not add a practical application because it merely recites “data gathering, analysis, and display” using generic hardware. It limits the underlying abstract idea to a particular technological environment but fails to provide a concrete, inventive improvement to computer functionality or a transformative use.
With respect to claim 13, the additional element merely recites generic data manipulation, routine computer functions, and conventional mathematical mapping without improving computer technology. The element fails to improve the operational efficiency, functioning, or capability of the computer itself, serving merely as a generalized instruction to apply a conventional concept to a specific dataset.
For claim 14, the additional elements fail to integrate the abstract idea into a practical application because they merely recite generic data collection, real-time updating, and standard computer execution without improving underlying computer functionality or machine performance. Receiving, accumulating, updating, and aligning data in "real time" are standard data-handling and mental processes that humans or generic processors can perform conceptually.
For claim 15, the claim merely recites standard data gathering and define a generic technological environment. They amount to insignificant extra-solution activity rather than transforming an abstract idea into a patent-eligible invention. Reciting the reception of data is a classic data-gathering limitation. Merely gathering, receiving, or storing data in conjunction with an abstract idea does not impose meaningful limits on the claim. It does not solve a technological problem or improve an underlying process.
For claim 16, recited additional element of a generic device (a 3-dimensional scanner) to perform routine actions (scanning an object) is simply gathering or receiving data and under the Alice/Mayo framework is considered conventional. The use of a standard scanner provides no transformative, “significantly more” element that elevates the claim to a practical application.
The recited elements fail to add a practical application because they merely recite generic data manipulation steps, importing, extracting, aligning, and integrating digital data sets, which describe a fundamental abstract idea of mathematical comparison and data reformatting implemented using conventional, generalized computer components.
These limitations describe mere data collection, data gathering, and the application of abstract mathematical and mental processes, and lack the necessary integrative steps to transform them into a practical application per MPEP 2106.
Under the MPEP 2106.05(g) guidelines regarding insignificant extra-solution activity, the mere act of crunching, gathering data on a conventional computing system is well-understood, routine, and conventional in the art of bioinformatics pipelines. The recited limitations serve solely as data-gathering or analyzing activities. Because these additional elements do not reflect any specific improvement to computer functioning or physical technology, the claim fails to integrate the judicial exception into a practical application, and instead amounts to insignificant, routine post-solution activity.
There are no limitations that indicate that the three-dimensional scanning process requires anything other than a conventional image scanner attached to a conventional computer to execute the instructions (a series of steps). As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-17 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, routine and conventional activity as evidenced by review article by Marradi et al. (Appl. Sci. 2020, 10, 5354) where he describes the use of 3D scanner for gathering dental image data and how it can be superimposed to other 3D data of the same object to get shared images (overlapped) with greater accuracy (Abstract; Figure 1; pg. 3)
As discussed above, there are no additional limitations to indicate that the claimed scan data processing system requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Furthermore, the additional elements recited in the claims amount to well-understood, routine and conventional activity. As such, the combination of additional elements recited in the claims is well-understood, routine and conventional.
The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-17 are not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1, 4-11, 15, and 16 are rejected under 35 U.S.C. 103 as obvious over Marshall et al. (US 8,199,988 B2) in view of Fisker et al. (US 8,837,026 B2).
Regarding claim 1:
Marshall et al. discloses a method and system for combining a first 3D data set (a preexisting digital dental model, i.e., import data) with a second 3D data set obtained by scanning an object with a three-dimensional scanner. The method includes:
receiving import data comprising shape information of at least part of an object (a dental model generated from prior scanning or CAD (col. 4, lines 5-35);
Generating scan data by scanning the same object with a 3D scanner (a second medical or optical scanner (col. 5, lines 10-40);
Extracting a common region between the import data and the scan data (identifying overlapping surface regions for registration (col. 6, lines 15-45);
Performing alignment based on the common region (best-fit registration using ICP-type algorithms (col. 6, lines 40-60; Figure 7); and
Generating integrating data by integrating the import data and scan data (merging the dental model with medical scan) (col. 4, lines 25-40; Figure 8).
Thus, Marshall et al. teaches most of the limitations of claim 1.
1. The import data reception step (receiving import data comprising shape information of part of an object from a source other than the 3D scanner-Marshall’ dental model derived from optical or CT scan (col. 3-4);
2. The scan data reception step (generating scan data comprising shape information and feature information by scanning the object with a 3D scanner, (col. 5);
3. The extraction step (extracting from the import data a region common to the scan data, (col. 6)
4. The alignment steps (performing alignment based on common regions) (Fig. 7, col.6) and
5. Integration step (generating integrated data by integrating the import and scan data. (Fig. 8, col 4)
Marshall et al. further discloses that the import data includes shape information such as mesh surfaces and surface normal (col. 4, lines 25-55; Figure 7), and that the scan data includes both shape information and feature information derived from the scanned object (col. 5, lines 25-55)
However, Marshall et al. does not explicitly teach a 3D scan data processing system in which pre-existing shape information imported into a computer to guide scanning.
Fisker et al. discloses a 3D scan data processing system and method in which pre-existing shape information (a CAD model or prior-scan data) is imported into a computer and used to guide scanning of a physical object, with alignment between the imported shape information and the subsequently generated scan data. Specifically, Fisker et al. teaches entering shape information of a physical object into a computer (Abstract; col. 3, lines, 10-40), generating scan data by scanning the object with a 3D scanner (col. 4, lines 5-35), and aligning the shape information and scan data to a common coordinate system (col. 3, 50-70; col. 7, lines 20-40)
Although Marshall et al. not explicitly use the term “import data,” this gap is addressed by Fisker et al. teaches the “importation” of shape information from a computer model into a scanning system, which serves as the direct functional equivalent.
A PHOSITA would have been motivated to combine Marshall et al.’s integration workflow with Fisker et al.’s importation step because Marshall et al. itself is directed to combining a first, preexisting 3D data set with a second 3D data set obtained by scanning the same object, and discloses receiving import data comprising shape information of at least part of an object, including mesh surfaces and surface normal, from a source such as prior scanning or CAD (col. 4, lines 5-55; Figure 7), generating scan data by scanning that same object with a three-dimensional scanner (col. 5, lines 10-40), extracting a common region between the import data and the scan data (col. 6, lines 15-45), performing alignment based on that common region using best-fit, ICP-type registration (col. 6, lines 40-60; Figure 7), and generating integrated data by integrating the import data and the scan data (col. 4, lines 25-40; Figure 8). Marshall et al., however, does not itself describe a system in which the pre-existing shape information is imported into a computer to guide the scanning process. Fisker et al. supplies this missing teaching, disclosing a 3D scan data processing system and method in which pre-existing shape information, such as a CAD model or prior-scan data, is imported into a computer and used to guide scanning of a physical object, with alignment between the imported shape information and the subsequently generated scan data (Abstract; col. 3, lines 10-40; col. 4, lines 5-35; col. 3, lines 50-70; col. 7, lines 20-40). Because Fisker et al.’s importation step performs the identical function – supplying preexisting shape data as “import data” for subsequent alignment with newly generated scan data – as the import data reception step already disclosed by Marshall et al., a PHOSITA would have recognized Fisker et al.’s teaching of “importation” of shape information from a computer model into a scanning system as the direct functional equivalent of, and a substitute for, Marshall et al.’s import data source.
A PHOSITA would have had a reasonable expectation of success in this combination because each reference performs its own discrete, known role exactly as it is already disclosed. Marshall et al. already discloses the scan data reception step, the extraction step, the alignment step, and the integration step recited in claim 1 (col. 5; col. 6; Fig. 7, col. 6; Fig. 8, col. 4), so combining it with Fisker et al.’s importation teaching leaves the function of those steps entirely unchanged; it merely supplies, from Fisker et al., an express teaching that the import data can be obtained by importing pre-existing shape information into the processing device before scanning, rather than relying solely on Marshall et al.’s disclosed dental-model source (col. 3-4). Because both references share the common goal of combining pre-existing digital shape data with newly acquired scans of the same object, and because Fisker et al.’s coordinate-system alignment of imported shape information with scan data (col. 3, lines 50-70; col. 7, lines 20-40) is directly compatible with Marshall et al.’s own best-fit registration and integration steps (col. 6, lines 40-60; col. 4, lines 25-40), a PHOSITA would have predictably and successfully arrived at a data processor processing device that imports import data, a scan data reception module, an extraction module, an alignment module, and an integration module, exactly as recited in claim 1.
Because these teachings are fundamentally aligned – both directed to combining pre-existing digital shape data with newly acquired 3D scan data of the same object, combining them yields highly predictable results without altering the function of either system. This combination therefore represents nothing more than the use of a known technique (Fisker et al.’s importation and pre-scan alignment of shape information) to improve a similar, known method (Marshall et al.’s import-and-scan-data integration workflow) in the same way, yielding predictable results, consistent with the KSR obviousness rationale.
Regarding claim 4:
Claim 4 requires a display step in which a display processor displays the 3D shape of the object on a display device.
Marshall et al. discloses displaying the 3D shapes defined by one or both data sets on a display device (col. 9, lines 55-65, Figure 2; Figure 9-18 showing the integrated 3D model displayed to the user).
Fisker et al. similarly discloses displaying the 3D model during scanning (col. 8, lines 55-70).
Thus, the combined teaching of Marshall et al. and Fisker et al. maps to the claim limitation of “a display step in which a display processor displays, on a display device, a three-dimensional shape of the object defined by at least one selected from the group of the import data and the scan data.”
Regarding claim 5:
Claim 5 adds an editing step in which an editing tool receives a command to edit part of the import data and updates the import data before the integration step.
Marshall et al. discloses that the dental model (import data) can be edited to add or remove structures prior to integration with the scan data (col. 13, lines 1-30) suggesting the limitation of “an editing step in which an editing tool receives an editing command to edit a part of the import data and updates the import data, wherein the integration step is performed after the editing step is performed.
Regarding claim 6:
Claim 6 requires selective display of the import data and scan data shapes based on a user command through an input device.
Marshall et al. discloses user-interactive display in which different 3D models can be toggled or selectively shown (col. 10, lines 5-30; col. 12, lines 45-65; col. 13, lines 35-55) suggesting the limitation of “the import data and the scan data, on the display device according to a command received through an input device.” Selective display of multiple data layers in response to user commands is well-established and routine feature of 3D visualization software before the effective filing date.
Regarding claim 7:
Claim 7 specifies that the display processor voxelizes import data and scan data before displaying.
Marshall et al. discloses processing 3D surface meshes for display (col. 12, lines 55-65; col. 14, lines 60-70; Figure 10) suggesting the limitation of “the display step, the display module voxelizes each of the import data and scan data and displays the voxelized data on the display device.”
Voxelization of point cloud data for display purposes was well known well before the effective filing date (US 7420555-transforming point cloud data to volumetric data) and (US 9754405-system, method and computer - readable medium for organizing and rendering 3d voxel models in a tree structure).
While Marshall et al. discloses processing 3D surface meshes for display, it does not explicitly describe voxelizing this data. However, the voxelization of scan data for display was well-known in the industry long before the effective filing date.
For example, prior art such as US 7420555 teaches transforming point cloud data into volumetric voxel data for display. Similarly, US 9754405 discloses voxel rendering methods for 3D scan data.
A person having ordinary skill in the art (PHOSITA) at the time of the effective filing date would have been motivated to apply these known voxelization rendering techniques to Marshall et al.'s display process. Voxelization was and still is widely recognized as a routine design choice to improve rendering speed and processing efficiency, yielding highly predictable visual results.
Regarding claim 8-10:
Claim 8 requires displaying the import and scan data shapes in different colors.
Claim 9 requires displaying regions in different colors according to data reliability and
Claim 10 requires setting the reliability of import data to the highest value.
Marshall et al. discloses rendering different data sets in distinct visual representations to allow the users to distinguish them (col. 20, lines 15-40; col. 21, lines 25-60; Fig 26-29) suggesting the limitation of “claim 8-10 stated above.”
Color coding of 3D scan data to indicate data source or reliability was a standard visualization technique well before the effective filing date.
A PHOSITA would have been motivated to apply known color-coded visualization to Marshall et al.’s display to facilitate distinguishing the import data (which was known, predetermined characteristics) from the scan data, and would naturally set import data reliability to a high/default value. This is a routine design choice with predictable results.
Regarding claim 11:
Claim 11 requires that the import data further comprises normal vectors indicating point directions and/or meshes formed by connecting points.
Marshall et al. discloses that the dental model (import data) includes surface mesh geometry (polygonal mesh) formed by connecting points, and associated surface normal vectors (col. 4, lines 40-70; Figure 21 and 23) suggesting the limitation of “at least one of a normal vector indicating directions of points of the import data and meshes formed by connecting the points.”
Regarding claim 15:
Claim 15 requires that the import data is received without using the 3D dimensional scanner, while the scan data is generated through the 3D-dimensional scanner.
Marshall et al. distinguishes the two data acquisition modalities: the dental model (import data) may be generated from a separate CT or optical scanner, or from a CAD system, without use of the scanning device employed to generate the scan data (col. 3, lines 1-30; col. 5, lines 1-15) suggesting the limitation of “receives the import data without using the three- dimensional scanner, and in the scan data reception step”.
Fisker et al. similarly teaches entering pre-existing shape information into the computer without using the scanning device (col. 3, lines 5-50).
Thus, the claimed invention is fully disclosed by the combined teachings.
Regarding claim 16:
Claim 16 recites a 3D scan data processing system that import 3D data including shape information and a scan data reception system configured to generate scan data by scanning with 3D scanner.
Marshall et al.’s disclosed system that includes a data processor, scan data reception processor, extraction processor, alignment processor, and integration processor, each performing the corresponding function disclosed in claim 16 (col. 2, lines 1-60; col. 9, lines 1-40; col. 10, lines 15-50) suggesting the limitation of “a data processor configured to import data defining a three- dimensional shape, a scan data reception processor configured to generate, by scanning the object with a three-dimensional scanner, an extraction processor configured to extract a region common to the scan data; an alignment processor configured to perform alignment based on the region common; and an integration processor configured to generate integrated data by integrating the import data and the scan data.”
Fisker et al.’s system similarly includes corresponding hardware components. (Col. 1, lines 1-30; Figure 1)
Claim 2, 3, 12 and 17 are rejected under 35 U.S.C. 103 as obvious over Marshall et al. in view of Fisker et al. as applied to the claims 1, 4-11, 15, and 16 above and further in view of He at al. (Sensors 2017, 17, 1862)
Marshall et al. in view of Fisker et al. are applied to the claims 1, 4-11, 15, and 16.
Regarding claim 2 and 17:
Claim 2 and 17 require that the extraction step generates feature information from the import data, and that the feature information comprises at least one of surface curvature information, surface corrugation information, or user input information.
Marshall et al. discloses extracting common regions from the import data, including surface mesh features of the dental model (col. 12, lines 55-65; col. 14, lines 60-70; Figure 10), which correspond to “shape features including surface curvature.”
However, Marshall et al. in view of Fisker et al. does not explicitly teach surface curvature surface normal in relation 3D scan registration.
He et al. teaches using geometric features of point clouds-specifically curvature, surface normal, and point cloud density-as feature information to find correspondence relationships for 3D scan registration (Abstract; pg. 2, middle; pg. 6, bottom) suggesting the limitation of “at least one selected from the group of surface curvature information, surface corrugation information, and user input information” and
The combination of Marshall et al.’s region extraction with He et al.’s curvature-based feature extraction represents a predictable application of known techniques. At the effective filing date, a PHOSITA would have been motivated to improve the accuracy and speed of Marshall et al.’s alignment step by incorporating He et al.’s curvature-based feature identification. Both references are directed to the same core problem of accurate 3D point cloud registration.
Furthermore, He et al. teaches extracting key points from a point cloud and using these geometric features to identify correspondences and map common points.
At the effective filing date, a PHOSITA would have been motivated to improve the accuracy and speed of Marshall et al.’s alignment step by incorporating He et al.’s curvature-based feature identification, because both references are directed to the same core problem of accurate 3D point cloud/surface registration. He et al. further teaches extracting key points from a point cloud and using these geometric features to identify correspondences and map common points (Abstract; pg. 2, middle; pg. 6, bottom), which is the same correspondence-identification function that Marshall et al.’s common-region extraction and best-fit alignment steps already perform (col. 6, lines 15-60; Figure 7). While Marshall et al. provides the foundational framework for aligning import data to scan data, He et al.’s curvature-based correspondence serves as a standardized method to enhance registration accuracy. Because the teachings of both references deal directly with optimizing point matching using geometric surface features, substituting or integrating He et al.’s feature identification into Marshall et al.’s process would predictably improve, without altering the underlying function of either system, the registration accuracy already targeted by Marshall et al.’s alignment step.
Thus, their combination would have been obvious to a PHOSITA at the time of the effective filing date. Marshall et al.’s extraction module and He et al.’s curvature/surface-normal feature detection each perform their own known, discrete role – generic surface feature extraction and specific geometric correspondence feature extraction, respectively as each reference already discloses, so combining them yields nothing more than the predictable result of an extraction module that generates feature information comprising surface curvature information, surface corrugation information, or user input information, consistent with the KSR rationale that the combination of known elements according to known methods to yield predictable results.
Regarding claim 3:
Claim 3 require that the extraction processor generates surface curvature or corrugation information by connecting adjacent points, and that alignment is performed by identifying common points based on feature information and mapping them.
He et al.’s GF-ICP algorithm teaches constructing curvature and normal features from adjacent points in a point cloud and using these features to identify correspondences and map common points (pg. 2, bottom; Figure 2) suggesting the limitation of “connecting adjacent points of the import data, as feature information of a three-dimensional shape of the object, and in the alignment step, the import data and the scan data are aligned with each other by identifying common points based on the import data and feature information of the scan data and mapping the common points to each other.”
Regarding claim 12:
Claim 12 adds a resolution determination step where a resolution processor determines whether the distance between adjacent import data points exceeds a reference distance (Maximum allowable distance in the scan data), and if so, generates new interpolated points to bring the import data to an equal or finer resolution.
Neither Marshall et al. nor Fisker et al. addresses this resolution normalization.
However, He et al. teach that robust ICP and feature-based registration, both point clouds must be at compatible spatial resolutions, and that up sampling and resampling of sparser models to match denser scan data is standard pre-processing (pg. 6, bottom; pg. 7, bottom) suggesting the limitation of “the resolution processor updates the import data by generating new points between at least some points such that the distance between the adjacent points of the import data is equal to or less than the reference distance, wherein the integration step is performed after the resolution determination step and the import data updating step are performed.”
Claim 13 and 14 are rejected under 35 U.S.C. 103 as obvious over Marshall et al. in view of Fisker et al. as applied to claims 1, 4-11, 15 and 16 above and further in view of Moore et al. (US 6920242 B1)
Marshall et al. in in view of Fisker et al. are applied to claims 1, 4-11, 15 and 16.
Regarding claim 13:
Claim 13 requires a common point reception step in which an input processor receives, through an input device, at least one use-specified common point at identical location in both the import data and scan data shapes, and uses these to align the two data sets.
According to claim 1 rejection, Marshall et al. teaches automatic alignment via best-fit registration.
Marshall et al. in in view of Fisker et al. does not teach explicitly use of point cloud assembly in which overlapping regions between scans used to align the scans.
Moore et al. discloses point cloud assembly in which overlapping regions between multiple scans are identified and used to align the scans. Moore et al. also discloses user -specified geometric reference points as an alternative to automatic feature matching, allowing users to manually indicate common points on two data sets to facilitate alignment (Abstract; col 3, 1-30; col. 4, lines 30-55) suggesting the limitation of “the alignment processor aligns the import data and the scan data with each other by a method in which the input processor maps the common point of the import data and the scan data that is received in the common point reception step.”
Marshall et al. teaches automatic alignment via best-fit registration, which is highly efficient for aligning overlapping datasets. However, automatic methods can fail when point clouds lack sufficient overlapping geometry or contain complex surfaces.
To overcome this, Moore et al. teaches that user-specified common points (manual registration) are a known, effective alternative alignment approach when automatic methods fall short. In 3D scan registration, combining automatic and manual techniques is a standard industry approach.
A PHOSITA (person having ordinary skill in the art) at the effective filing date would have been motivated to combine these teachings. Marshall et al. teaches automatic alignment via best-fit registration (col. 6, lines 40-60; Figure 7), which is highly efficient for aligning overlapping datasets, but automatic methods of this type can fail when point clouds lack sufficient overlapping geometry or contain complex surfaces. Moore et al. addresses this limitation, teaching point cloud assembly in which overlapping regions between multiple scans are identified and used to align the scans, and further teaching user-specified geometric reference points as an alternative to automatic feature matching, whereby a user manually indicates common points on two data sets to facilitate alignment (Abstract; col. 3, lines 1-30; col. 4, lines 30-55). Specifically, they would use Moore et al.’s manual common-point specification as a necessary supplement to Marshall’s automatic alignment, because Moore et al.’s teaching of mapping user-specified common points between two data sets (Abstract; col. 3, lines 1-30; col. 4, lines 30-55) performs the identical alignment function as Marshall et al.’s best-fit registration, but by an alternative, manually-guided mechanism suited to the cases in which automatic registration is unreliable.
Because Moore et al.’s user-specified common-point mapping performs, as an alternative alignment mechanism, exactly the same alignment function already disclosed by Marshall et al.’s best-fit registration, the combination represents nothing more than the substitution of a known alignment technique for use in the specific circumstances where the primary technique is unreliable, yielding the predictable and expected result of a common point reception step and alignment step as recited in claim 13, consistent with the KSR rationale that combining familiar elements according to known methods to yield predictable results.
Regarding claim 14:
Claim 14 requires that the scan data generated in the scan data reception step is data accumulated in real time by the 3D scanner, and that the alignment step and integration step are performed while the scan data is updated in real time.
Moore et al. discloses real-time point cloud assembly and alignment, noting that the disclosed system enables image acquisition and rendering in real time as data is accumulated from the 3D scanner (col. 2, lines50-65; col. 5, lines 5-25) suggesting the limitation of “in the scan data reception step is data accumulated in real time by the three-dimensional scanner, and the alignment step and the integration step are performed while the scan data accumulated in real time by the scan data reception step is updated.”
Marshall et al.’s integration workflow is not limited post-scan data; applying Moore et al.’s real time accumulation teaching to Marshall’s alignment and integration steps is an obvious combination where a PHOSITA would be motivated by the desire for efficient, on-the-fly processing a standard engineering goal in 3D scanning systems, with predictable results.
Conclusion
No claims are allowed.
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/A.H.K./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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